103 research outputs found
A Sparse Graph-Structured Lasso Mixed Model for Genetic Association with Confounding Correction
While linear mixed model (LMM) has shown a competitive performance in
correcting spurious associations raised by population stratification, family
structures, and cryptic relatedness, more challenges are still to be addressed
regarding the complex structure of genotypic and phenotypic data. For example,
geneticists have discovered that some clusters of phenotypes are more
co-expressed than others. Hence, a joint analysis that can utilize such
relatedness information in a heterogeneous data set is crucial for genetic
modeling.
We proposed the sparse graph-structured linear mixed model (sGLMM) that can
incorporate the relatedness information from traits in a dataset with
confounding correction. Our method is capable of uncovering the genetic
associations of a large number of phenotypes together while considering the
relatedness of these phenotypes. Through extensive simulation experiments, we
show that the proposed model outperforms other existing approaches and can
model correlation from both population structure and shared signals. Further,
we validate the effectiveness of sGLMM in the real-world genomic dataset on two
different species from plants and humans. In Arabidopsis thaliana data, sGLMM
behaves better than all other baseline models for 63.4% traits. We also discuss
the potential causal genetic variation of Human Alzheimer's disease discovered
by our model and justify some of the most important genetic loci.Comment: Code available at https://github.com/YeWenting/sGLM
Beyond Pixels: Exploring Human-Readable SVG Generation for Simple Images with Vision Language Models
In the field of computer graphics, the use of vector graphics, particularly
Scalable Vector Graphics (SVG), represents a notable development from
traditional pixel-based imagery. SVGs, with their XML-based format, are
distinct in their ability to directly and explicitly represent visual elements
such as shape, color, and path. This direct representation facilitates a more
accurate and logical depiction of graphical elements, enhancing reasoning and
interpretability. Recognizing the potential of SVGs, the machine learning
community has introduced multiple methods for image vectorization. However,
transforming images into SVG format while retaining the relational properties
and context of the original scene remains a key challenge. Most vectorization
methods often yield SVGs that are overly complex and not easily interpretable.
In response to this challenge, we introduce our method, Simple-SVG-Generation
(S\textsuperscript{2}VG\textsuperscript{2}). Our method focuses on producing
SVGs that are both accurate and simple, aligning with human readability and
understanding. With simple images, we evaluate our method with reasoning tasks
together with advanced language models, the results show a clear improvement
over previous SVG generation methods. We also conducted surveys for human
evaluation on the readability of our generated SVGs, the results also favor our
methods.Comment: 10 pages, 7 figure
BadLabel: A Robust Perspective on Evaluating and Enhancing Label-noise Learning
Label-noise learning (LNL) aims to increase the model's generalization given
training data with noisy labels. To facilitate practical LNL algorithms,
researchers have proposed different label noise types, ranging from
class-conditional to instance-dependent noises. In this paper, we introduce a
novel label noise type called BadLabel, which can significantly degrade the
performance of existing LNL algorithms by a large margin. BadLabel is crafted
based on the label-flipping attack against standard classification, where
specific samples are selected and their labels are flipped to other labels so
that the loss values of clean and noisy labels become indistinguishable. To
address the challenge posed by BadLabel, we further propose a robust LNL method
that perturbs the labels in an adversarial manner at each epoch to make the
loss values of clean and noisy labels again distinguishable. Once we select a
small set of (mostly) clean labeled data, we can apply the techniques of
semi-supervised learning to train the model accurately. Empirically, our
experimental results demonstrate that existing LNL algorithms are vulnerable to
the newly introduced BadLabel noise type, while our proposed robust LNL method
can effectively improve the generalization performance of the model under
various types of label noise. The new dataset of noisy labels and the source
codes of robust LNL algorithms are available at
https://github.com/zjfheart/BadLabels
Foundation Model-oriented Robustness: Robust Image Model Evaluation with Pretrained Models
Machine learning has demonstrated remarkable performance over finite
datasets, yet whether the scores over the fixed benchmarks can sufficiently
indicate the model's performance in the real world is still in discussion. In
reality, an ideal robust model will probably behave similarly to the oracle
(e.g., the human users), thus a good evaluation protocol is probably to
evaluate the models' behaviors in comparison to the oracle. In this paper, we
introduce a new robustness measurement that directly measures the image
classification model's performance compared with a surrogate oracle (i.e., a
foundation model). Besides, we design a simple method that can accomplish the
evaluation beyond the scope of the benchmarks. Our method extends the image
datasets with new samples that are sufficiently perturbed to be distinct from
the ones in the original sets, but are still bounded within the same
image-label structure the original test image represents, constrained by a
foundation model pretrained with a large amount of samples. As a result, our
new method will offer us a new way to evaluate the models' robustness
performance, free of limitations of fixed benchmarks or constrained
perturbations, although scoped by the power of the oracle. In addition to the
evaluation results, we also leverage our generated data to understand the
behaviors of the model and our new evaluation strategies
Tuning the Magnetic Ordering Temperature of Hexagonal Ferrites by Structural Distortion Control
To tune the magnetic properties of hexagonal ferrites, a family of
magnetoelectric multiferroic materials, by atomic-scale structural engineering,
we studied the effect of structural distortion on the magnetic ordering
temperature (TN). Using the symmetry analysis, we show that unlike most
antiferromagnetic rare-earth transition-metal perovskites, a larger structural
distortion leads to a higher TN in hexagonal ferrites and manganites, because
the K3 structural distortion induces the three-dimensional magnetic ordering,
which is forbidden in the undistorted structure by symmetry. We also revealed a
near-linear relation between TN and the tolerance factor and a power-law
relation between TN and the K3 distortion amplitude. Following the analysis, a
record-high TN (185 K) among hexagonal ferrites was predicted in hexagonal
ScFeO3 and experimentally verified in epitaxially stabilized films. These
results add to the paradigm of spin-lattice coupling in antiferromagnetic
oxides and suggests further tunability of hexagonal ferrites if more lattice
distortion can be achieved
Iterative Few-shot Semantic Segmentation from Image Label Text
Few-shot semantic segmentation aims to learn to segment unseen class objects
with the guidance of only a few support images. Most previous methods rely on
the pixel-level label of support images. In this paper, we focus on a more
challenging setting, in which only the image-level labels are available. We
propose a general framework to firstly generate coarse masks with the help of
the powerful vision-language model CLIP, and then iteratively and mutually
refine the mask predictions of support and query images. Extensive experiments
on PASCAL-5i and COCO-20i datasets demonstrate that our method not only
outperforms the state-of-the-art weakly supervised approaches by a significant
margin, but also achieves comparable or better results to recent supervised
methods. Moreover, our method owns an excellent generalization ability for the
images in the wild and uncommon classes. Code will be available at
https://github.com/Whileherham/IMR-HSNet.Comment: ijcai 202
Noncollinear spin structure in Fe3+xCo3−xTi2 (x = 0, 2, 3) from neutron diffraction
Neutron powder diffraction has been used to investigate the spin structure of the hard-magnetic alloy Fe3+xCo3−xTi2 (x = 0, 2, 3). The materials are produced by rapid quenching from the melt, they possess a hexagonal crystal structure, and they are nanocrystalline with crystallite sizes D of the order of 40 nm. Projections of the magnetic moment onto both the crystalline c axis and the basal plane were observed. The corresponding misalignment angle exhibits a nonlinear decrease with x, which we explain as a micromagnetic effect caused by Fe-Co site disorder. The underlying physics is a special kind of random-anisotropy magnetism that leads to the prediction of 1/D1/4 power-law dependence of the misalignment angle on the crystallite size
Anisotropic optical and magnetic response in self-assembled TiN-CoFe\u3csub\u3e2\u3c/sub\u3e nanocomposites
Transition metal nitrides (e.g., TiN) have shown tremendous promise in optical metamaterials for nanophotonic devices due to their plasmonic properties comparable to noble metals and superior high temperature stability. Vertically aligned nanocomposites (VANs) offer a great platform for combining two dissimilar functional materials with a one-step deposition technique toward multifunctionality integration and strong structural/property anisotropy. Here we report a two-phase nanocomposite design combining ferromagnetic CoFe2 nanosheets in the plasmonic TiN matrix as a new hybrid plasmonic metamaterial. The hybrid metamaterials exhibit obvious anisotropic optical and magnetic responses, as well as a pronounced magneto-optical coupling response evidenced by MOKE measurement, owing to the novel vertically aligned structure. This work demonstrates a new TiN-based metamaterial with anisotropic properties and multi-functionality towards optical switchable spintronics, magnetic sensors and integrated optic
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